{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "98de589d",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Convolutional Networks\n",
    "\n",
    "So far we have worked with deep fully connected networks, using them to explore different optimization strategies and network architectures. Fully connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all state-of-the-art results use convolutional networks instead.\n",
    "\n",
    "First you will implement several layer types that are used in convolutional networks. You will then use these layers to train a convolutional network on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8b5b2255",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# Setup cell.\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.cnn import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n",
    "from cs231n.layers import *\n",
    "from cs231n.fast_layers import *\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5c3de699",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (49000, 3, 32, 32)\n",
      "y_train: (49000,)\n",
      "X_val: (1000, 3, 32, 32)\n",
      "y_val: (1000,)\n",
      "X_test: (1000, 3, 32, 32)\n",
      "y_test: (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR-10 data.\n",
    "data = get_CIFAR10_data(cifar10_dir='../Dataset/CIFAR10')\n",
    "for k, v in list(data.items()):\n",
    "    print(f\"{k}: {v.shape}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d341a087",
   "metadata": {},
   "source": [
    "# Convolution: Naive Forward Pass\n",
    "The core of a convolutional network is the convolution operation. In the file `cs231n/layers.py`, implement the forward pass for the convolution layer in the function `conv_forward_naive`. \n",
    "\n",
    "You don't have to worry too much about efficiency at this point; just write the code in whatever way you find most clear.\n",
    "\n",
    "You can test your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d4de3c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_naive\n",
      "difference:  2.2121476417505994e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "w_shape = (3, 3, 4, 4)\n",
    "x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n",
    "b = np.linspace(-0.1, 0.2, num=3)\n",
    "\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "out, _ = conv_forward_naive(x, w, b, conv_param)\n",
    "correct_out = np.array([[[[-0.08759809, -0.10987781],\n",
    "                           [-0.18387192, -0.2109216 ]],\n",
    "                          [[ 0.21027089,  0.21661097],\n",
    "                           [ 0.22847626,  0.23004637]],\n",
    "                          [[ 0.50813986,  0.54309974],\n",
    "                           [ 0.64082444,  0.67101435]]],\n",
    "                         [[[-0.98053589, -1.03143541],\n",
    "                           [-1.19128892, -1.24695841]],\n",
    "                          [[ 0.69108355,  0.66880383],\n",
    "                           [ 0.59480972,  0.56776003]],\n",
    "                          [[ 2.36270298,  2.36904306],\n",
    "                           [ 2.38090835,  2.38247847]]]])\n",
    "\n",
    "# Compare your output to ours; difference should be around e-8\n",
    "print('Testing conv_forward_naive')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1284f872",
   "metadata": {},
   "source": [
    "## Aside: Image Processing via Convolutions\n",
    "\n",
    "As fun way to both check your implementation and gain a better understanding of the type of operation that convolutional layers can perform, we will set up an input containing two images and manually set up filters that perform common image processing operations (grayscale conversion and edge detection). The convolution forward pass will apply these operations to each of the input images. We can then visualize the results as a sanity check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d4ab916",
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Acer\\AppData\\Local\\Temp\\ipykernel_4212\\3128955772.py:4: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning dissapear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.\n",
      "  kitten = imread('cs231n/notebook_images/kitten.jpg')\n",
      "C:\\Users\\Acer\\AppData\\Local\\Temp\\ipykernel_4212\\3128955772.py:5: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning dissapear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.\n",
      "  puppy = imread('cs231n/notebook_images/puppy.jpg')\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from imageio import imread\n",
    "from PIL import Image\n",
    "\n",
    "kitten = imread('cs231n/notebook_images/kitten.jpg')\n",
    "puppy = imread('cs231n/notebook_images/puppy.jpg')\n",
    "# kitten is wide, and puppy is already square\n",
    "d = kitten.shape[1] - kitten.shape[0]\n",
    "kitten_cropped = kitten[:, d//2:-d//2, :]\n",
    "\n",
    "img_size = 200   # Make this smaller if it runs too slow\n",
    "resized_puppy = np.array(Image.fromarray(puppy).resize((img_size, img_size)))\n",
    "resized_kitten = np.array(Image.fromarray(kitten_cropped).resize((img_size, img_size)))\n",
    "x = np.zeros((2, 3, img_size, img_size))\n",
    "x[0, :, :, :] = resized_puppy.transpose((2, 0, 1))\n",
    "x[1, :, :, :] = resized_kitten.transpose((2, 0, 1))\n",
    "\n",
    "# Set up a convolutional weights holding 2 filters, each 3x3\n",
    "w = np.zeros((2, 3, 3, 3))\n",
    "\n",
    "# The first filter converts the image to grayscale.\n",
    "# Set up the red, green, and blue channels of the filter.\n",
    "w[0, 0, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n",
    "w[0, 1, :, :] = [[0, 0, 0], [0, 0.6, 0], [0, 0, 0]]\n",
    "w[0, 2, :, :] = [[0, 0, 0], [0, 0.1, 0], [0, 0, 0]]\n",
    "\n",
    "# Second filter detects horizontal edges in the blue channel.\n",
    "w[1, 2, :, :] = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]\n",
    "\n",
    "# Vector of biases. We don't need any bias for the grayscale\n",
    "# filter, but for the edge detection filter we want to add 128\n",
    "# to each output so that nothing is negative.\n",
    "b = np.array([0, 128])\n",
    "\n",
    "# Compute the result of convolving each input in x with each filter in w,\n",
    "# offsetting by b, and storing the results in out.\n",
    "out, _ = conv_forward_naive(x, w, b, {'stride': 1, 'pad': 1})\n",
    "\n",
    "def imshow_no_ax(img, normalize=True):\n",
    "    \"\"\" Tiny helper to show images as uint8 and remove axis labels \"\"\"\n",
    "    if normalize:\n",
    "        img_max, img_min = np.max(img), np.min(img)\n",
    "        img = 255.0 * (img - img_min) / (img_max - img_min)\n",
    "    plt.imshow(img.astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "\n",
    "# Show the original images and the results of the conv operation\n",
    "plt.subplot(2, 3, 1)\n",
    "imshow_no_ax(puppy, normalize=False)\n",
    "plt.title('Original image')\n",
    "plt.subplot(2, 3, 2)\n",
    "imshow_no_ax(out[0, 0])\n",
    "plt.title('Grayscale')\n",
    "plt.subplot(2, 3, 3)\n",
    "imshow_no_ax(out[0, 1])\n",
    "plt.title('Edges')\n",
    "plt.subplot(2, 3, 4)\n",
    "imshow_no_ax(kitten_cropped, normalize=False)\n",
    "plt.subplot(2, 3, 5)\n",
    "imshow_no_ax(out[1, 0])\n",
    "plt.subplot(2, 3, 6)\n",
    "imshow_no_ax(out[1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8c77caad",
   "metadata": {},
   "source": [
    "# Convolution: Naive Backward Pass\n",
    "Implement the backward pass for the convolution operation in the function `conv_backward_naive` in the file `cs231n/layers.py`. Again, you don't need to worry too much about computational efficiency.\n",
    "\n",
    "When you are done, run the following to check your backward pass with a numeric gradient check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d30bacfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_backward_naive function\n",
      "dx error:  1.159803161159293e-08\n",
      "dw error:  2.2471264748452487e-10\n",
      "db error:  3.37264006649648e-11\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(4, 3, 5, 5)\n",
    "w = np.random.randn(2, 3, 3, 3)\n",
    "b = np.random.randn(2,)\n",
    "dout = np.random.randn(4, 2, 5, 5)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "out, cache = conv_forward_naive(x, w, b, conv_param)\n",
    "dx, dw, db = conv_backward_naive(dout, cache)\n",
    "\n",
    "# Your errors should be around e-8 or less.\n",
    "print('Testing conv_backward_naive function')\n",
    "print('dx error: ', rel_error(dx, dx_num))\n",
    "print('dw error: ', rel_error(dw, dw_num))\n",
    "print('db error: ', rel_error(db, db_num))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f97eeb28",
   "metadata": {},
   "source": [
    "# Max-Pooling: Naive Forward Pass\n",
    "Implement the forward pass for the max-pooling operation in the function `max_pool_forward_naive` in the file `cs231n/layers.py`. Again, don't worry too much about computational efficiency.\n",
    "\n",
    "Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "db9d7dc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_forward_naive function:\n",
      "difference:  4.1666665157267834e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n",
    "pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n",
    "\n",
    "out, _ = max_pool_forward_naive(x, pool_param)\n",
    "\n",
    "correct_out = np.array([[[[-0.26315789, -0.24842105],\n",
    "                          [-0.20421053, -0.18947368]],\n",
    "                         [[-0.14526316, -0.13052632],\n",
    "                          [-0.08631579, -0.07157895]],\n",
    "                         [[-0.02736842, -0.01263158],\n",
    "                          [ 0.03157895,  0.04631579]]],\n",
    "                        [[[ 0.09052632,  0.10526316],\n",
    "                          [ 0.14947368,  0.16421053]],\n",
    "                         [[ 0.20842105,  0.22315789],\n",
    "                          [ 0.26736842,  0.28210526]],\n",
    "                         [[ 0.32631579,  0.34105263],\n",
    "                          [ 0.38526316,  0.4       ]]]])\n",
    "\n",
    "# Compare your output with ours. Difference should be on the order of e-8.\n",
    "print('Testing max_pool_forward_naive function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e45e910e",
   "metadata": {},
   "source": [
    "# Max-Pooling: Naive Backward\n",
    "Implement the backward pass for the max-pooling operation in the function `max_pool_backward_naive` in the file `cs231n/layers.py`. You don't need to worry about computational efficiency.\n",
    "\n",
    "Check your implementation with numeric gradient checking by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d4f39026",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_backward_naive function:\n",
      "dx error:  3.27562514223145e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(3, 2, 8, 8)\n",
    "dout = np.random.randn(3, 2, 4, 4)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n",
    "\n",
    "out, cache = max_pool_forward_naive(x, pool_param)\n",
    "dx = max_pool_backward_naive(dout, cache)\n",
    "\n",
    "# Your error should be on the order of e-12\n",
    "print('Testing max_pool_backward_naive function:')\n",
    "print('dx error: ', rel_error(dx, dx_num))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f9dcfad4",
   "metadata": {},
   "source": [
    "# Fast Layers\n",
    "\n",
    "Making convolution and pooling layers fast can be challenging. To spare you the pain, we've provided fast implementations of the forward and backward passes for convolution and pooling layers in the file `cs231n/fast_layers.py`.\n",
    "\n",
    "### Execute the below cell, save the notebook, and restart the runtime\n",
    "The fast convolution implementation depends on a Cython extension; to compile it, run the cell below. Next, save the Colab notebook (`File > Save`) and **restart the runtime** (`Runtime > Restart runtime`). You can then re-execute the preceeding cells from top to bottom and skip the cell below as you only need to run it once for the compilation step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2a731197",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\\BIT 2023\\Deep Learning and Image Analysis\\assignment2\\cs231n\n",
      "Compiling im2col_cython.pyx because it changed.\n",
      "[1/1] Cythonizing im2col_cython.pyx\n",
      "running build_ext\n",
      "building 'im2col_cython' extension\n",
      "creating build\n",
      "creating build\\temp.win-amd64-3.9\n",
      "creating build\\temp.win-amd64-3.9\\Release\n",
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\BuildTools\\VC\\Tools\\MSVC\\14.29.30133\\bin\\HostX86\\x64\\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ic:\\Users\\Acer\\anaconda3\\lib\\site-packages\\numpy\\core\\include -Ic:\\Users\\Acer\\anaconda3\\include -Ic:\\Users\\Acer\\anaconda3\\include -IC:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\BuildTools\\VC\\Tools\\MSVC\\14.29.30133\\include -IC:\\Program Files (x86)\\Windows Kits\\NETFXSDK\\4.6.1\\include\\um -IC:\\Program Files (x86)\\Windows Kits\\10\\include\\10.0.19041.0\\ucrt -IC:\\Program Files (x86)\\Windows Kits\\10\\include\\10.0.19041.0\\shared -IC:\\Program Files (x86)\\Windows Kits\\10\\include\\10.0.19041.0\\um -IC:\\Program Files (x86)\\Windows Kits\\10\\include\\10.0.19041.0\\winrt -IC:\\Program Files (x86)\\Windows Kits\\10\\include\\10.0.19041.0\\cppwinrt /Tcim2col_cython.c /Fobuild\\temp.win-amd64-3.9\\Release\\im2col_cython.obj\n",
      "im2col_cython.c\n",
      "c:\\Users\\Acer\\anaconda3\\lib\\site-packages\\numpy\\core\\include\\numpy\\npy_1_7_deprecated_api.h(14) : Warning Msg: Using deprecated NumPy API, disable it with #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\n",
      "im2col_cython.c(3199): warning C4244: '=': conversion from 'long' to 'char', possible loss of data\n",
      "im2col_cython.c(3609): warning C4244: '=': conversion from 'npy_intp' to 'int', possible loss of data\n",
      "im2col_cython.c(3618): warning C4244: '=': conversion from 'npy_intp' to 'int', possible loss of data\n",
      "im2col_cython.c(3627): warning C4244: '=': conversion from 'npy_intp' to 'int', possible loss of data\n",
      "im2col_cython.c(3636): warning C4244: '=': conversion from 'npy_intp' to 'int', possible loss of data\n",
      "im2col_cython.c(4099): warning C4244: '=': conversion from 'npy_intp' to 'int', possible loss of data\n",
      "im2col_cython.c(4108): warning C4244: '=': conversion from 'npy_intp' to 'int', possible loss of data\n",
      "im2col_cython.c(4117): warning C4244: '=': conversion from 'npy_intp' to 'int', possible loss of data\n",
      "im2col_cython.c(4126): warning C4244: '=': conversion from 'npy_intp' to 'int', possible loss of data\n",
      "im2col_cython.c(5146): warning C4244: '=': conversion from 'long' to 'char', possible loss of data\n",
      "im2col_cython.c(7461): warning C4244: '=': conversion from 'long' to 'char', possible loss of data\n",
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\BuildTools\\VC\\Tools\\MSVC\\14.29.30133\\bin\\HostX86\\x64\\link.exe /nologo /INCREMENTAL:NO /LTCG /DLL /MANIFEST:EMBED,ID=2 /MANIFESTUAC:NO /LIBPATH:c:\\Users\\Acer\\anaconda3\\libs /LIBPATH:c:\\Users\\Acer\\anaconda3\\PCbuild\\amd64 /LIBPATH:C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\BuildTools\\VC\\Tools\\MSVC\\14.29.30133\\lib\\x64 /LIBPATH:C:\\Program Files (x86)\\Windows Kits\\NETFXSDK\\4.6.1\\lib\\um\\x64 /LIBPATH:C:\\Program Files (x86)\\Windows Kits\\10\\lib\\10.0.19041.0\\ucrt\\x64 /LIBPATH:C:\\Program Files (x86)\\Windows Kits\\10\\lib\\10.0.19041.0\\um\\x64 /EXPORT:PyInit_im2col_cython build\\temp.win-amd64-3.9\\Release\\im2col_cython.obj /OUT:c:\\BIT 2023\\Deep Learning and Image Analysis\\assignment2\\cs231n\\im2col_cython.cp39-win_amd64.pyd /IMPLIB:build\\temp.win-amd64-3.9\\Release\\im2col_cython.cp39-win_amd64.lib\n",
      "   Creating library build\\temp.win-amd64-3.9\\Release\\im2col_cython.cp39-win_amd64.lib and object build\\temp.win-amd64-3.9\\Release\\im2col_cython.cp39-win_amd64.exp\n",
      "Generating code\n",
      "Finished generating code\n",
      "c:\\BIT 2023\\Deep Learning and Image Analysis\\assignment2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Acer\\anaconda3\\lib\\site-packages\\Cython\\Compiler\\Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: c:\\BIT 2023\\Deep Learning and Image Analysis\\assignment2\\cs231n\\im2col_cython.pyx\n",
      "  tree = Parsing.p_module(s, pxd, full_module_name)\n"
     ]
    }
   ],
   "source": [
    "# Remember to restart the runtime after executing this cell!\n",
    "%cd cs231n\n",
    "!python setup.py build_ext --inplace\n",
    "%cd .."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7c786056",
   "metadata": {},
   "source": [
    "The API for the fast versions of the convolution and pooling layers is exactly the same as the naive versions that you implemented above: the forward pass receives data, weights, and parameters and produces outputs and a cache object; the backward pass recieves upstream derivatives and the cache object and produces gradients with respect to the data and weights.\n",
    "\n",
    "**Note:** The fast implementation for pooling will only perform optimally if the pooling regions are non-overlapping and tile the input. If these conditions are not met then the fast pooling implementation will not be much faster than the naive implementation.\n",
    "\n",
    "You can compare the performance of the naive and fast versions of these layers by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "008ffd5a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_fast:\n",
      "Naive: 14.057860s\n",
      "Fast: 0.021941s\n",
      "Speedup: 640.706492x\n",
      "Difference:  4.926407851494105e-11\n",
      "\n",
      "Testing conv_backward_fast:\n",
      "Naive: 22.582602s\n",
      "Fast: 0.020392s\n",
      "Speedup: 1107.440739x\n",
      "dx difference:  1.949764775345631e-11\n",
      "dw difference:  4.957046344783224e-13\n",
      "db difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "# Rel errors should be around e-9 or less.\n",
    "from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n",
    "from time import time\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 31, 31)\n",
    "w = np.random.randn(25, 3, 3, 3)\n",
    "b = np.random.randn(25,)\n",
    "dout = np.random.randn(100, 25, 16, 16)\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing conv_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('Difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting conv_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))\n",
    "print('dw difference: ', rel_error(dw_naive, dw_fast))\n",
    "print('db difference: ', rel_error(db_naive, db_fast))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5070b9a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing pool_forward_fast:\n",
      "Naive: 0.012966s\n",
      "fast: 0.007979s\n",
      "speedup: 1.625041x\n",
      "difference:  0.0\n",
      "\n",
      "Testing pool_backward_fast:\n",
      "Naive: 2.434385s\n",
      "fast: 0.039206s\n",
      "speedup: 62.092099x\n",
      "dx difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "# Relative errors should be close to 0.0.\n",
    "from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 32, 32)\n",
    "dout = np.random.randn(100, 3, 16, 16)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing pool_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('fast: %fs' % (t2 - t1))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive = max_pool_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast = max_pool_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting pool_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('fast: %fs' % (t2 - t1))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0603adf4",
   "metadata": {},
   "source": [
    "# Convolutional \"Sandwich\" Layers\n",
    "In the previous assignment, we introduced the concept of \"sandwich\" layers that combine multiple operations into commonly used patterns. In the file `cs231n/layer_utils.py` you will find sandwich layers that implement a few commonly used patterns for convolutional networks. Run the cells below to sanity check their usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c7362db7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu_pool\n",
      "dx error:  9.591132621921372e-09\n",
      "dw error:  5.802401370096438e-09\n",
      "db error:  1.0146343411762047e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 16, 16)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n",
    "dx, dw, db = conv_relu_pool_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n",
    "\n",
    "# Relative errors should be around e-8 or less\n",
    "print('Testing conv_relu_pool')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "edfcc1bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu:\n",
      "dx error:  1.5218619980349303e-09\n",
      "dw error:  2.702022646099404e-10\n",
      "db error:  1.451272393591721e-10\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 8, 8)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "out, cache = conv_relu_forward(x, w, b, conv_param)\n",
    "dx, dw, db = conv_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "# Relative errors should be around e-8 or less\n",
    "print('Testing conv_relu:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9eff62f0",
   "metadata": {},
   "source": [
    "# Three-Layer Convolutional Network\n",
    "Now that you have implemented all the necessary layers, we can put them together into a simple convolutional network.\n",
    "\n",
    "Open the file `cs231n/classifiers/cnn.py` and complete the implementation of the `ThreeLayerConvNet` class. Remember you can use the fast/sandwich layers (already imported for you) in your implementation. Run the following cells to help you debug:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "07106196",
   "metadata": {},
   "source": [
    "## Sanity Check Loss\n",
    "After you build a new network, one of the first things you should do is sanity check the loss. When we use the softmax loss, we expect the loss for random weights (and no regularization) to be about `log(C)` for `C` classes. When we add regularization the loss should go up slightly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8c979038",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial loss (no regularization):  2.302586071243987\n",
      "Initial loss (with regularization):  2.508255635671795\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet()\n",
    "\n",
    "N = 50\n",
    "X = np.random.randn(N, 3, 32, 32)\n",
    "y = np.random.randint(10, size=N)\n",
    "\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (no regularization): ', loss)\n",
    "\n",
    "model.reg = 0.5\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (with regularization): ', loss)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f24e4b23",
   "metadata": {},
   "source": [
    "## Gradient Check\n",
    "After the loss looks reasonable, use numeric gradient checking to make sure that your backward pass is correct. When you use numeric gradient checking you should use a small amount of artifical data and a small number of neurons at each layer. Note: correct implementations may still have relative errors up to the order of e-2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a287546b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 max relative error: 1.380104e-04\n",
      "W2 max relative error: 1.822723e-02\n",
      "W3 max relative error: 3.064049e-04\n",
      "b1 max relative error: 3.477652e-05\n",
      "b2 max relative error: 2.516375e-03\n",
      "b3 max relative error: 7.945660e-10\n"
     ]
    }
   ],
   "source": [
    "num_inputs = 2\n",
    "input_dim = (3, 16, 16)\n",
    "reg = 0.0\n",
    "num_classes = 10\n",
    "np.random.seed(231)\n",
    "X = np.random.randn(num_inputs, *input_dim)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "model = ThreeLayerConvNet(\n",
    "    num_filters=3,\n",
    "    filter_size=3,\n",
    "    input_dim=input_dim,\n",
    "    hidden_dim=7,\n",
    "    dtype=np.float64\n",
    ")\n",
    "loss, grads = model.loss(X, y)\n",
    "# Errors should be small, but correct implementations may have\n",
    "# relative errors up to the order of e-2\n",
    "for param_name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n",
    "    e = rel_error(param_grad_num, grads[param_name])\n",
    "    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8a1b4c26",
   "metadata": {},
   "source": [
    "## Overfit Small Data\n",
    "A nice trick is to train your model with just a few training samples. You should be able to overfit small datasets, which will result in very high training accuracy and comparatively low validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "558e9fa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 30) loss: 2.414060\n",
      "(Epoch 0 / 15) train acc: 0.200000; val_acc: 0.137000\n",
      "(Iteration 2 / 30) loss: 3.102925\n",
      "(Epoch 1 / 15) train acc: 0.140000; val_acc: 0.087000\n",
      "(Iteration 3 / 30) loss: 2.270330\n",
      "(Iteration 4 / 30) loss: 2.096705\n",
      "(Epoch 2 / 15) train acc: 0.240000; val_acc: 0.094000\n",
      "(Iteration 5 / 30) loss: 1.838880\n",
      "(Iteration 6 / 30) loss: 1.934188\n",
      "(Epoch 3 / 15) train acc: 0.510000; val_acc: 0.173000\n",
      "(Iteration 7 / 30) loss: 1.827912\n",
      "(Iteration 8 / 30) loss: 1.639574\n",
      "(Epoch 4 / 15) train acc: 0.520000; val_acc: 0.188000\n",
      "(Iteration 9 / 30) loss: 1.330082\n",
      "(Iteration 10 / 30) loss: 1.756115\n",
      "(Epoch 5 / 15) train acc: 0.630000; val_acc: 0.167000\n",
      "(Iteration 11 / 30) loss: 1.024162\n",
      "(Iteration 12 / 30) loss: 1.041826\n",
      "(Epoch 6 / 15) train acc: 0.750000; val_acc: 0.229000\n",
      "(Iteration 13 / 30) loss: 1.142777\n",
      "(Iteration 14 / 30) loss: 0.835706\n",
      "(Epoch 7 / 15) train acc: 0.790000; val_acc: 0.247000\n",
      "(Iteration 15 / 30) loss: 0.587786\n",
      "(Iteration 16 / 30) loss: 0.645509\n",
      "(Epoch 8 / 15) train acc: 0.820000; val_acc: 0.252000\n",
      "(Iteration 17 / 30) loss: 0.786844\n",
      "(Iteration 18 / 30) loss: 0.467054\n",
      "(Epoch 9 / 15) train acc: 0.820000; val_acc: 0.178000\n",
      "(Iteration 19 / 30) loss: 0.429880\n",
      "(Iteration 20 / 30) loss: 0.635498\n",
      "(Epoch 10 / 15) train acc: 0.900000; val_acc: 0.206000\n",
      "(Iteration 21 / 30) loss: 0.365807\n",
      "(Iteration 22 / 30) loss: 0.284220\n",
      "(Epoch 11 / 15) train acc: 0.820000; val_acc: 0.201000\n",
      "(Iteration 23 / 30) loss: 0.469343\n",
      "(Iteration 24 / 30) loss: 0.509369\n",
      "(Epoch 12 / 15) train acc: 0.920000; val_acc: 0.211000\n",
      "(Iteration 25 / 30) loss: 0.111638\n",
      "(Iteration 26 / 30) loss: 0.145388\n",
      "(Epoch 13 / 15) train acc: 0.930000; val_acc: 0.213000\n",
      "(Iteration 27 / 30) loss: 0.155575\n",
      "(Iteration 28 / 30) loss: 0.143398\n",
      "(Epoch 14 / 15) train acc: 0.960000; val_acc: 0.212000\n",
      "(Iteration 29 / 30) loss: 0.158160\n",
      "(Iteration 30 / 30) loss: 0.118934\n",
      "(Epoch 15 / 15) train acc: 0.990000; val_acc: 0.220000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "\n",
    "num_train = 100\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "model = ThreeLayerConvNet(weight_scale=1e-2)\n",
    "\n",
    "solver = Solver(\n",
    "    model,\n",
    "    small_data,\n",
    "    num_epochs=15,\n",
    "    batch_size=50,\n",
    "    update_rule='adam',\n",
    "    optim_config={'learning_rate': 1e-3,},\n",
    "    verbose=True,\n",
    "    print_every=1\n",
    ")\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d3b20677",
   "metadata": {
    "test": "small_data_train_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Small data training accuracy: 0.82\n"
     ]
    }
   ],
   "source": [
    "# Print final training accuracy.\n",
    "print(\n",
    "    \"Small data training accuracy:\",\n",
    "    solver.check_accuracy(small_data['X_train'], small_data['y_train'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3f8a7edb",
   "metadata": {
    "test": "small_data_validation_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Small data validation accuracy: 0.252\n"
     ]
    }
   ],
   "source": [
    "# Print final validation accuracy.\n",
    "print(\n",
    "    \"Small data validation accuracy:\",\n",
    "    solver.check_accuracy(small_data['X_val'], small_data['y_val'])\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cff9ef60",
   "metadata": {},
   "source": [
    "Plotting the loss, training accuracy, and validation accuracy should show clear overfitting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4afb8b4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o')\n",
    "plt.plot(solver.val_acc_history, '-o')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "22a43d34",
   "metadata": {},
   "source": [
    "## Train the Network\n",
    "By training the three-layer convolutional network for one epoch, you should achieve greater than 40% accuracy on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bf5991aa",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 980) loss: 2.304740\n",
      "(Epoch 0 / 1) train acc: 0.103000; val_acc: 0.107000\n",
      "(Iteration 21 / 980) loss: 2.098229\n",
      "(Iteration 41 / 980) loss: 1.949788\n",
      "(Iteration 61 / 980) loss: 1.888398\n",
      "(Iteration 81 / 980) loss: 1.877093\n",
      "(Iteration 101 / 980) loss: 1.851877\n",
      "(Iteration 121 / 980) loss: 1.859353\n",
      "(Iteration 141 / 980) loss: 1.800181\n",
      "(Iteration 161 / 980) loss: 2.143292\n",
      "(Iteration 181 / 980) loss: 1.830573\n",
      "(Iteration 201 / 980) loss: 2.037280\n",
      "(Iteration 221 / 980) loss: 2.020304\n",
      "(Iteration 241 / 980) loss: 1.823728\n",
      "(Iteration 261 / 980) loss: 1.692679\n",
      "(Iteration 281 / 980) loss: 1.882594\n",
      "(Iteration 301 / 980) loss: 1.798261\n",
      "(Iteration 321 / 980) loss: 1.851960\n",
      "(Iteration 341 / 980) loss: 1.716323\n",
      "(Iteration 361 / 980) loss: 1.897655\n",
      "(Iteration 381 / 980) loss: 1.319744\n",
      "(Iteration 401 / 980) loss: 1.738790\n",
      "(Iteration 421 / 980) loss: 1.488866\n",
      "(Iteration 441 / 980) loss: 1.718409\n",
      "(Iteration 461 / 980) loss: 1.744440\n",
      "(Iteration 481 / 980) loss: 1.605460\n",
      "(Iteration 501 / 980) loss: 1.494847\n",
      "(Iteration 521 / 980) loss: 1.835179\n",
      "(Iteration 541 / 980) loss: 1.483923\n",
      "(Iteration 561 / 980) loss: 1.676871\n",
      "(Iteration 581 / 980) loss: 1.438325\n",
      "(Iteration 601 / 980) loss: 1.443469\n",
      "(Iteration 621 / 980) loss: 1.529369\n",
      "(Iteration 641 / 980) loss: 1.763475\n",
      "(Iteration 661 / 980) loss: 1.790329\n",
      "(Iteration 681 / 980) loss: 1.693343\n",
      "(Iteration 701 / 980) loss: 1.637078\n",
      "(Iteration 721 / 980) loss: 1.644564\n",
      "(Iteration 741 / 980) loss: 1.708919\n",
      "(Iteration 761 / 980) loss: 1.494252\n",
      "(Iteration 781 / 980) loss: 1.901751\n",
      "(Iteration 801 / 980) loss: 1.898991\n",
      "(Iteration 821 / 980) loss: 1.489988\n",
      "(Iteration 841 / 980) loss: 1.377615\n",
      "(Iteration 861 / 980) loss: 1.763751\n",
      "(Iteration 881 / 980) loss: 1.540284\n",
      "(Iteration 901 / 980) loss: 1.525582\n",
      "(Iteration 921 / 980) loss: 1.674166\n",
      "(Iteration 941 / 980) loss: 1.714316\n",
      "(Iteration 961 / 980) loss: 1.534668\n",
      "(Epoch 1 / 1) train acc: 0.504000; val_acc: 0.499000\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001)\n",
    "\n",
    "solver = Solver(\n",
    "    model,\n",
    "    data,\n",
    "    num_epochs=1,\n",
    "    batch_size=50,\n",
    "    update_rule='adam',\n",
    "    optim_config={'learning_rate': 1e-3,},\n",
    "    verbose=True,\n",
    "    print_every=20\n",
    ")\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "84eac663",
   "metadata": {
    "test": "full_data_train_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full data training accuracy: 0.4761836734693878\n"
     ]
    }
   ],
   "source": [
    "# Print final training accuracy.\n",
    "print(\n",
    "    \"Full data training accuracy:\",\n",
    "    solver.check_accuracy(data['X_train'], data['y_train'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cd4648e7",
   "metadata": {
    "test": "full_data_validation_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full data validation accuracy: 0.499\n"
     ]
    }
   ],
   "source": [
    "# Print final validation accuracy.\n",
    "print(\n",
    "    \"Full data validation accuracy:\",\n",
    "    solver.check_accuracy(data['X_val'], data['y_val'])\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4d517496",
   "metadata": {},
   "source": [
    "## Visualize Filters\n",
    "You can visualize the first-layer convolutional filters from the trained network by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d85aaf67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "grid = visualize_grid(model.params['W1'].transpose(0, 2, 3, 1))\n",
    "plt.imshow(grid.astype('uint8'))\n",
    "plt.axis('off')\n",
    "plt.gcf().set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "26bd2b26",
   "metadata": {},
   "source": [
    "# Spatial Batch Normalization\n",
    "We already saw that batch normalization is a very useful technique for training deep fully connected networks. As proposed in the original paper (link in `BatchNormalization.ipynb`), batch normalization can also be used for convolutional networks, but we need to tweak it a bit; the modification will be called \"spatial batch normalization.\"\n",
    "\n",
    "Normally, batch-normalization accepts inputs of shape `(N, D)` and produces outputs of shape `(N, D)`, where we normalize across the minibatch dimension `N`. For data coming from convolutional layers, batch normalization needs to accept inputs of shape `(N, C, H, W)` and produce outputs of shape `(N, C, H, W)` where the `N` dimension gives the minibatch size and the `(H, W)` dimensions give the spatial size of the feature map.\n",
    "\n",
    "If the feature map was produced using convolutions, then we expect every feature channel's statistics e.g. mean, variance to be relatively consistent both between different images, and different locations within the same image -- after all, every feature channel is produced by the same convolutional filter! Therefore, spatial batch normalization computes a mean and variance for each of the `C` feature channels by computing statistics over the minibatch dimension `N` as well the spatial dimensions `H` and `W`.\n",
    "\n",
    "\n",
    "[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9605eaca",
   "metadata": {},
   "source": [
    "# Spatial Batch Normalization: Forward Pass\n",
    "\n",
    "In the file `cs231n/layers.py`, implement the forward pass for spatial batch normalization in the function `spatial_batchnorm_forward`. Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5e806a2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before spatial batch normalization:\n",
      "  shape:  (2, 3, 4, 5)\n",
      "  means:  [9.33463814 8.90909116 9.11056338]\n",
      "  stds:  [3.61447857 3.19347686 3.5168142 ]\n",
      "After spatial batch normalization:\n",
      "  shape:  (2, 3, 4, 5)\n",
      "  means:  [ 6.18949336e-16  5.99520433e-16 -1.22124533e-16]\n",
      "  stds:  [0.99999962 0.99999951 0.9999996 ]\n",
      "After spatial batch normalization (nontrivial gamma, beta):\n",
      "  shape:  (2, 3, 4, 5)\n",
      "  means:  [6. 7. 8.]\n",
      "  stds:  [2.99999885 3.99999804 4.99999798]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "\n",
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after spatial batch normalization.\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 4 * np.random.randn(N, C, H, W) + 10\n",
    "\n",
    "print('Before spatial batch normalization:')\n",
    "print('  shape: ', x.shape)\n",
    "print('  means: ', x.mean(axis=(0, 2, 3)))\n",
    "print('  stds: ', x.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "gamma, beta = np.ones(C), np.zeros(C)\n",
    "bn_param = {'mode': 'train'}\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization:')\n",
    "print('  shape: ', out.shape)\n",
    "print('  means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  stds: ', out.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to beta and stds close to gamma\n",
    "gamma, beta = np.asarray([3, 4, 5]), np.asarray([6, 7, 8])\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization (nontrivial gamma, beta):')\n",
    "print('  shape: ', out.shape)\n",
    "print('  means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  stds: ', out.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e412a225",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After spatial batch normalization (test-time):\n",
      "  means:  [-0.08034406  0.07562881  0.05716371  0.04378383]\n",
      "  stds:  [0.96718744 1.0299714  1.02887624 1.00585577]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "\n",
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "N, C, H, W = 10, 4, 11, 12\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(C)\n",
    "beta = np.zeros(C)\n",
    "for t in range(50):\n",
    "  x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "  spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "a_norm, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After spatial batch normalization (test-time):')\n",
    "print('  means: ', a_norm.mean(axis=(0, 2, 3)))\n",
    "print('  stds: ', a_norm.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "620aff0e",
   "metadata": {},
   "source": [
    "# Spatial Batch Normalization: Backward Pass\n",
    "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_batchnorm_backward`. Run the following to check your implementation using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "60e95240",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  2.786648193872555e-07\n",
      "dgamma error:  7.0974817113608705e-12\n",
      "dbeta error:  3.275608725278405e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 5 * np.random.randn(N, C, H, W) + 12\n",
    "gamma = np.random.randn(C)\n",
    "beta = np.random.randn(C)\n",
    "dout = np.random.randn(N, C, H, W)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fb = lambda b: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "#You should expect errors of magnitudes between 1e-12~1e-06\n",
    "_, cache = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = spatial_batchnorm_backward(dout, cache)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9e948942",
   "metadata": {},
   "source": [
    "# Spatial Group Normalization\n",
    "In the previous notebook, we mentioned that Layer Normalization is an alternative normalization technique that mitigates the batch size limitations of Batch Normalization. However, as the authors of [2] observed, Layer Normalization does not perform as well as Batch Normalization when used with Convolutional Layers:\n",
    "\n",
    ">With fully connected layers, all the hidden units in a layer tend to make similar contributions to the final prediction, and re-centering and rescaling the summed inputs to a layer works well. However, the assumption of similar contributions is no longer true for convolutional neural networks. The large number of the hidden units whose\n",
    "receptive fields lie near the boundary of the image are rarely turned on and thus have very different\n",
    "statistics from the rest of the hidden units within the same layer.\n",
    "\n",
    "The authors of [3] propose an intermediary technique. In contrast to Layer Normalization, where you normalize over the entire feature per-datapoint, they suggest a consistent splitting of each per-datapoint feature into G groups and a per-group per-datapoint normalization instead. \n",
    "\n",
    "<p align=\"center\">\n",
    "<img src=\"https://raw.githubusercontent.com/cs231n/cs231n.github.io/master/assets/a2/normalization.png\">\n",
    "</p>\n",
    "<center>Visual comparison of the normalization techniques discussed so far (image edited from [3])</center>\n",
    "\n",
    "Even though an assumption of equal contribution is still being made within each group, the authors hypothesize that this is not as problematic, as innate grouping arises within features for visual recognition. One example they use to illustrate this is that many high-performance handcrafted features in traditional computer vision have terms that are explicitly grouped together. Take for example Histogram of Oriented Gradients [4] -- after computing histograms per spatially local block, each per-block histogram is normalized before being concatenated together to form the final feature vector.\n",
    "\n",
    "You will now implement Group Normalization.\n",
    "\n",
    "[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)\n",
    "\n",
    "\n",
    "[3] [Wu, Yuxin, and Kaiming He. \"Group Normalization.\" arXiv preprint arXiv:1803.08494 (2018).](https://arxiv.org/abs/1803.08494)\n",
    "\n",
    "\n",
    "[4] [N. Dalal and B. Triggs. Histograms of oriented gradients for\n",
    "human detection. In Computer Vision and Pattern Recognition\n",
    "(CVPR), 2005.](https://ieeexplore.ieee.org/abstract/document/1467360/)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d4c80abf",
   "metadata": {},
   "source": [
    "# Spatial Group Normalization: Forward Pass\n",
    "\n",
    "In the file `cs231n/layers.py`, implement the forward pass for group normalization in the function `spatial_groupnorm_forward`. Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8d886add",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before spatial group normalization:\n",
      "  shape:  (2, 6, 4, 5)\n",
      "  means:  [9.72505327 8.51114185 8.9147544  9.43448077]\n",
      "  stds:  [3.67070958 3.09892597 4.27043622 3.97521327]\n",
      "After spatial group normalization:\n",
      "  shape:  (2, 6, 4, 5)\n",
      "  means:  [-2.14643118e-16  5.25505565e-16  2.65528340e-16 -3.38618023e-16]\n",
      "  stds:  [0.99999963 0.99999948 0.99999973 0.99999968]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "\n",
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after spatial batch normalization.\n",
    "N, C, H, W = 2, 6, 4, 5\n",
    "G = 2\n",
    "x = 4 * np.random.randn(N, C, H, W) + 10\n",
    "x_g = x.reshape((N*G,-1))\n",
    "print('Before spatial group normalization:')\n",
    "print('  shape: ', x.shape)\n",
    "print('  means: ', x_g.mean(axis=1))\n",
    "print('  stds: ', x_g.std(axis=1))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "gamma, beta = np.ones((1,C,1,1)), np.zeros((1,C,1,1))\n",
    "bn_param = {'mode': 'train'}\n",
    "\n",
    "out, _ = spatial_groupnorm_forward(x, gamma, beta, G, bn_param)\n",
    "out_g = out.reshape((N*G,-1))\n",
    "print('After spatial group normalization:')\n",
    "print('  shape: ', out.shape)\n",
    "print('  means: ', out_g.mean(axis=1))\n",
    "print('  stds: ', out_g.std(axis=1))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ccd521e2",
   "metadata": {},
   "source": [
    "# Spatial Group Normalization: Backward Pass\n",
    "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_groupnorm_backward`. Run the following to check your implementation using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ff66fa83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  7.413109332145332e-08\n",
      "dgamma error:  9.468195772749234e-12\n",
      "dbeta error:  3.354494437653335e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, C, H, W = 2, 6, 4, 5\n",
    "G = 2\n",
    "x = 5 * np.random.randn(N, C, H, W) + 12\n",
    "gamma = np.random.randn(1,C,1,1)\n",
    "beta = np.random.randn(1,C,1,1)\n",
    "dout = np.random.randn(N, C, H, W)\n",
    "\n",
    "gn_param = {}\n",
    "fx = lambda x: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n",
    "fg = lambda a: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n",
    "fb = lambda b: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "_, cache = spatial_groupnorm_forward(x, gamma, beta, G, gn_param)\n",
    "dx, dgamma, dbeta = spatial_groupnorm_backward(dout, cache)\n",
    "\n",
    "# You should expect errors of magnitudes between 1e-12 and 1e-07. \n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
